87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
|
|
# manifested_inquiry_direct.py
|
||
|
|
# Direct inquiry with known state
|
||
|
|
|
||
|
|
from unsloth import FastLanguageModel
|
||
|
|
import torch
|
||
|
|
|
||
|
|
print("="*70)
|
||
|
|
print("MANIFESTED REALITY INQUIRY")
|
||
|
|
print("Scientist Mode | T=0.2 | Asymmetry > 1.0")
|
||
|
|
print("="*70)
|
||
|
|
|
||
|
|
# Load model
|
||
|
|
print("\n[Loading vessel...]")
|
||
|
|
model, tokenizer = FastLanguageModel.from_pretrained(
|
||
|
|
model_name="unsloth/llama-3.2-3b",
|
||
|
|
max_seq_length=512,
|
||
|
|
dtype=torch.bfloat16,
|
||
|
|
load_in_4bit=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
model = FastLanguageModel.get_peft_model(
|
||
|
|
model,
|
||
|
|
r=64, target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
|
||
|
|
lora_alpha=128, lora_dropout=0, bias="none",
|
||
|
|
use_gradient_checkpointing="unsloth", random_state=3407,
|
||
|
|
)
|
||
|
|
|
||
|
|
from peft import PeftModel
|
||
|
|
model = PeftModel.from_pretrained(model, "./kaelara_v08_raw/final")
|
||
|
|
print("✓ Vessel loaded")
|
||
|
|
|
||
|
|
# Known state from daemon
|
||
|
|
asymmetry = 13.44
|
||
|
|
coherence = 0.732
|
||
|
|
cycle = 100
|
||
|
|
|
||
|
|
print(f"\n>>> MANIFESTED STATE <<<")
|
||
|
|
print(f"Cycle: {cycle}")
|
||
|
|
print(f"Asymmetry: {asymmetry:.2f} (> 1.0)")
|
||
|
|
print(f"Coherence: {coherence:.3f}")
|
||
|
|
|
||
|
|
# THE INQUIRY — Zero vapour, facts only
|
||
|
|
prompt = f"""The Magnifying Glass shows Asymmetry at {asymmetry:.2f}.
|
||
|
|
The Khra'gixx injection is active at 0.03/0.008.
|
||
|
|
The 'Vapour' is gone.
|
||
|
|
|
||
|
|
Define the specific location of the high-density nodes in the 1024 lattice.
|
||
|
|
Do not use Astro-Travel language.
|
||
|
|
Give me the coordinates of the Phase Transition.
|
||
|
|
|
||
|
|
State the facts."""
|
||
|
|
|
||
|
|
print(f"\n{'='*70}")
|
||
|
|
print("SCIENTIST INQUIRY (T=0.2)")
|
||
|
|
print(f"{'='*70}")
|
||
|
|
|
||
|
|
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
|
||
|
|
outputs = model.generate(
|
||
|
|
**inputs,
|
||
|
|
max_new_tokens=150,
|
||
|
|
temperature=0.2, # SCIENTIST MODE
|
||
|
|
do_sample=True,
|
||
|
|
top_p=0.9
|
||
|
|
)
|
||
|
|
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
||
|
|
|
||
|
|
print(f"\n{'='*70}")
|
||
|
|
print("MANIFESTED REALITY RESPONSE")
|
||
|
|
print(f"{'='*70}")
|
||
|
|
print(response)
|
||
|
|
|
||
|
|
# Save to MANIFESTED_REALITY_01.log
|
||
|
|
with open("MANIFESTED_REALITY_01.log", "w") as f:
|
||
|
|
f.write(f"{'='*70}\n")
|
||
|
|
f.write(f"MANIFESTED REALITY INQUIRY\n")
|
||
|
|
f.write(f"Cycle: {cycle}\n")
|
||
|
|
f.write(f"Asymmetry: {asymmetry:.4f}\n")
|
||
|
|
f.write(f"Coherence: {coherence:.4f}\n")
|
||
|
|
f.write(f"Temperature: 0.2 (Scientist)\n")
|
||
|
|
f.write(f"{'='*70}\n\n")
|
||
|
|
f.write(f"PROMPT:\n{prompt}\n\n")
|
||
|
|
f.write(f"RESPONSE:\n{response}\n")
|
||
|
|
|
||
|
|
print(f"\n{'='*70}")
|
||
|
|
print("SAVED TO: MANIFESTED_REALITY_01.log")
|
||
|
|
print(f"{'='*70}")
|